MLS-C01 Exploratory Data Analysis Practice Test 20 — 15 Questions

Question 1

Which TWO of the following are best practices for exploratory data analysis when using Amazon SageMaker Data Wrangler? (Select TWO.)

Accepted Answer

Use Data Wrangler's built-in data visualizations to explore feature distributions and relationships.. Data Wrangler's built-in visualizations allow for quick exploration of feature distributions and relationships without leaving the tool, making it a best practice for EDA. Exporting the Data Wrangler flow as a Jupyter notebook enables reproducibility and sharing with the team. Storing intermediate results in Athena (A) is not a best practice specific to Data Wrangler; it adds overhead. Using EMR for data profiling (C) is unnecessary since Data Wrangler includes profiling capabilities. Exporting data to QuickSight before transformation (D) is not recommended; analysis should be done within Data Wrangler's transformation steps.

Answer

Store all intermediate results in Amazon Athena for querying.

Answer

Use Amazon EMR to run Spark jobs for data profiling.

Answer

Always export the data to Amazon QuickSight for analysis before transformation.

Question 2

A data scientist needs to understand the distribution of a numeric feature in a dataset stored in Amazon S3. Which AWS service can be used to run a quick exploratory query without setting up a server?

Accepted Answer

Amazon Athena. Amazon Athena allows serverless SQL queries on data in S3. Option A (Amazon Redshift) is a data warehouse; Option B (Amazon EMR) requires cluster setup; Option D (AWS Glue) is for ETL.

Answer

Amazon Redshift

Answer

Amazon EMR

Answer

AWS Glue

Question 3

A data scientist is analyzing a dataset with 10 million rows and 50 columns. The target variable is highly imbalanced (99% negative, 1% positive). Which approach is most appropriate for exploratory data analysis before modeling?

Accepted Answer

Take a stratified sample that preserves the 99:1 ratio.. C is correct because stratified sampling preserves the original class proportion (99:1) in the sample, which is important for exploratory data analysis on imbalanced data without artificially altering the distribution. Option A (remove all negatives) loses all negative data, preventing analysis of majority class patterns. Option B (random sample) may result in insufficient positive examples due to imbalance. Option D (up-sample minority) would change the distribution and could lead to misleading visualizations and statistics during EDA.

Answer

Remove all negative examples and analyze only the positive ones.

Answer

Take a random sample of 100,000 rows from the entire dataset.

Answer

Up-sample the minority class to balance the dataset before analysis.

Question 4

During exploratory data analysis, a data scientist notices that the correlation matrix of features shows many pairs with absolute correlation > 0.95. The dataset includes both numerical and categorical variables. Which technique is most appropriate to reduce multicollinearity while preserving the most information?

Accepted Answer

Apply Principal Component Analysis (PCA) to the features.. PCA is the most appropriate technique because it transforms correlated features into orthogonal principal components, effectively handling multicollinearity while preserving variance. Option B (using only one-hot encoded categorical features) discards numerical features and may not address multicollinearity; Option C (L1 regularization) is a modeling technique, not for EDA; Option D (removing one feature per highly correlated pair) is ad-hoc and can lose information compared to PCA which captures variance in fewer dimensions.

Answer

Use only one-hot encoded categorical features.

Answer

Apply L1 regularization during model training.

Answer

Remove one feature from each highly correlated pair.

Question 5

A data scientist is analyzing a dataset with a timestamp column. The goal is to identify seasonality and trends. Which visualization technique is most suitable?

Accepted Answer

Time series line plot of the target variable over time.. A time series line plot is the standard visualization for identifying trends and seasonality over time. Option B (box plot grouped by day of week) can show distributions but may not reveal trends or seasonality clearly. Option C (scatter plot of target vs. timestamp) can show patterns but may be less clear than a line plot for time series. Option D (heatmap of correlations) is for exploring relationships between features, not for time series analysis.

Answer

Box plot of the target variable grouped by day of week.

Answer

Scatter plot of the target variable vs. the timestamp.

Answer

Heatmap of correlation between all features.

Question 6

A company uses Amazon SageMaker Data Wrangler to perform exploratory data analysis. They want to detect outliers in a numerical column using the Interquartile Range (IQR) method. Which transformation should they apply in Data Wrangler?

Accepted Answer

Handle outliers. Amazon SageMaker Data Wrangler provides a 'Handle outliers' transform that supports IQR-based outlier detection. Option A (Impute) is used to fill missing values, not detect outliers. Option B (Normalize) scales data to a standard range. Option D (Binning) groups continuous values into intervals. Therefore, the correct transform to apply for IQR outlier detection is 'Handle outliers'.

Answer

Impute

Answer

Normalize

Answer

Binning

Question 7

A data scientist is performing EDA on a dataset with 1 million rows. They suspect the dataset contains duplicate rows. Which approach is most efficient to identify duplicates in Amazon SageMaker Studio?

Accepted Answer

Use DuckDB SQL query: SELECT COUNT(*) - COUNT(DISTINCT *) FROM table.. DuckDB is an in-process SQL OLAP database that can run on a single machine and efficiently handle large datasets. Option A (Python loop) is slow; Option B (pandas drop_duplicates) may be memory-intensive; Option D (Athena) is serverless but incurs cost and latency.

Answer

Write a Python script that loops through each row and compares to a set of seen rows.

Answer

Use pandas drop_duplicates and then check the length difference.

Answer

Use Amazon Athena to query the S3 data with COUNT(DISTINCT *).

Question 8

A data scientist is exploring a dataset with many missing values. They want to understand the pattern of missingness before deciding on imputation. Which approach is most appropriate?

Accepted Answer

Visualize the missingness using a heatmap or bar chart.. Visualizing missingness with a heatmap or bar chart (using libraries like missingno) reveals patterns such as MCAR, MAR, or MNAR. Option A (correlation matrix) does not directly show missingness patterns. Option B (dropping rows) may remove valuable data and assumes MCAR. Option C (mean imputation) also assumes MCAR and can bias results if missingness is not random.

Answer

Compute the correlation matrix of the features with missing values.

Answer

Drop all rows with any missing values.

Answer

Impute all missing values with the mean of each column.

Question 9

A data scientist is using Amazon SageMaker Studio notebooks for EDA. They want to share a reproducible report that includes code, visualizations, and narrative text with their team. Which approach should they use?

Accepted Answer

Save the notebook as an .ipynb file and share it via Amazon S3.. A Jupyter notebook (.ipynb) saved in SageMaker Studio contains code, visualizations, and narrative text, and sharing it via Amazon S3 allows team members to reproduce and interact with the analysis. Option B (SageMaker Clarify) is for bias detection and model explainability, not for sharing EDA reports. Option C (Amazon QuickSight) is used for interactive dashboards and does not include the underlying code. Option D (SageMaker Autopilot) automates model building and does not generate a shareable EDA report.

Answer

Use Amazon SageMaker Clarify to generate an EDA report.

Answer

Export the results to Amazon QuickSight and create a dashboard.

Answer

Use Amazon SageMaker Autopilot to generate a report.

Question 10

Which TWO approaches are appropriate for handling missing categorical data during exploratory data analysis? (Choose two.)

Accepted Answer

Impute with the mode (most frequent) of the column.. Options B and C are correct. Imputing with the mode (B) is a simple and effective method for categorical data, as it preserves the most frequent category without introducing new values. Treating missing values as a separate 'Unknown' category (C) allows the model to capture potential patterns associated with missingness, which can be informative. Option A is incorrect because one-hot encoding is a technique for representing categorical variables, not for handling missing data; it requires the values to be known first. Option D is incorrect because dropping rows with missing values can result in significant data loss and may introduce bias, especially if missingness is not random. Option E is incorrect because mean imputation is suitable for numerical data, not categorical data.

Answer

Use one-hot encoding to represent missingness as a binary feature.

Answer

Drop all rows with missing values in that column.

Answer

Impute missing values with the mean of the column.

Question 11

Which THREE actions are valid steps in exploratory data analysis when working with a new dataset? (Choose three.)

Accepted Answer

Check the data types of each column.. Options A, B, and E are correct. A: Checking data types is fundamental in EDA to understand the nature of each variable. B: Generating descriptive statistics (mean, std, min, max) provides a quick summary of central tendency, dispersion, and range for numerical features. E: Creating histograms helps visualize the distribution of numerical features, revealing skewness, outliers, or patterns. Option C is incorrect because fitting a linear regression model is a modeling step, not part of EDA. Option D is incorrect because splitting the dataset into training and test sets is for model validation, not for initial data exploration.

Answer

Fit a linear regression model to identify important features.

Answer

Split the dataset into training and test sets.

Question 12

Which TWO techniques can be used to detect multicollinearity among numerical features during exploratory data analysis? (Choose two.)

Accepted Answer

Compute a correlation matrix and look for pairs with absolute correlation > 0.8.. Multicollinearity indicates high correlation between predictors. Option B: Compute a correlation matrix and look for pairs with absolute correlation > 0.8 directly reveals linear dependencies. Option D: Variance Inflation Factor (VIF) measures how much the variance of a coefficient is inflated due to collinearity; VIF > 5 or 10 suggests multicollinearity. Option A (PCA) reduces dimensionality but does not directly detect collinearity. Option C (t-test) tests mean differences, not associations. Option E (chi-square) tests categorical independence, not applicable to numerical features.

Answer

Apply Principal Component Analysis (PCA) and examine loadings.

Answer

Perform a t-test between each pair of features.

Answer

Use a chi-square test of independence.

Question 13

Refer to the exhibit. A data scientist lists files in an S3 bucket. The dataset is split into train, test, and validation sets. What is the most likely issue with this data split?

Accepted Answer

The training set is smaller than the test set, which is unusual.. The training set (1024 bytes) is smaller than the test set (2048 bytes), which is unusual. Typically training set should be larger. Option A (missing header) cannot be inferred; Option B (CSV format) is fine; Option D (partitioning) is not evident.

Answer

The files are not partitioned by date.

Answer

The training file is missing a header row.

Answer

The test file should be in JSON format.

Question 14

Refer to the exhibit. A data scientist is unable to run an Amazon Athena query on data in `my-bucket`. The IAM policy shown is attached to the user. What is the most likely reason for the failure?

Accepted Answer

Athena needs s3:PutObject permission to write results.. Athena requires `s3:PutObject` permission to write query results to an output location. Without this permission, the query fails. Option A is not the issue because the bucket exists and is accessible; Option C is not necessarily relevant; Option D is incorrect because the bucket does exist (otherwise the user couldn't even attempt the query).

Answer

The ListBucket action is not granted.

Answer

The data is encrypted with SSE-C.

Answer

The bucket does not exist.

Question 15

Refer to the exhibit. A data scientist is running an Amazon EMR Spark job for exploratory data analysis on a large dataset. The job fails with the error shown. What is the most appropriate action to resolve this?

Accepted Answer

Increase the executor memory in Spark configuration.. The error message indicates an OutOfMemoryError in the Spark executors. Increasing executor memory (option C) directly addresses this by providing more heap space for data processing. Option A (fewer nodes) reduces total cluster memory, worsening the problem. Option B (Parquet format) can improve I/O performance but does not resolve insufficient memory allocation. Option D (increase driver memory) only helps the driver process, not the executors.

Answer

Reduce the number of worker nodes.

Answer

Convert the input data to Parquet format.

Answer

Increase the driver memory.